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A novel algorithm for color constancy
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Gamut Constrained Illuminant Estimation
International Journal of Computer Vision
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IEEE Transactions on Image Processing
Improving gamut mapping color constancy
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Scene illumination as an indicator of image manipulation
IH'10 Proceedings of the 12th international conference on Information hiding
Hybrid color space transformation to visualize color constancy
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Color constancy, intrinsic images, and shape estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Illuminant estimation from projections on the planckian locus
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Specularity, the zeta-image, and information-theoretic illuminant estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Multi-objective optimization based color constancy
Applied Soft Computing
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The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant.Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed.Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and real-world scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut mappings provide more stable solutions, and (4) the fusion strategy based on the intersection of feasible sets provides better color constancy results than the union of the feasible sets.